{
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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np # 数据处理最重要的模块\n",
    "import pandas as pd # 数据处理最重要的模块\n",
    "import scipy.stats as stats # 统计模块\n",
    "import scipy\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"matplotlib\")\n",
    "\n",
    "from datetime import datetime # 时间模块\n",
    "import statsmodels.formula.api as smf  # OLS regression\n",
    "\n",
    "# import pyreadr # read RDS file\n",
    "\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt  # 画图模块\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "from matplotlib.font_manager import FontProperties # 作图中文\n",
    "from pylab import mpl\n",
    "#mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "#输出矢量图 渲染矢量图\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell # jupyter运行输出的模块\n",
    "#显示每一个运行结果\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "#设置行不限制数量\n",
    "#pd.set_option('display.max_rows',None)\n",
    "\n",
    "#设置列不限制数量\n",
    "pd.set_option('display.max_columns', None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+--------------+------------------+\n",
      "|            |   Monthly PE |   Monthly Return |\n",
      "|------------+--------------+------------------|\n",
      "| 2013-01-31 |      2.60286 |      0.00251497  |\n",
      "| 2013-02-28 |      2.60577 |     -0.00047876  |\n",
      "| 2013-03-31 |      2.53527 |     -0.00256602  |\n",
      "| 2013-04-30 |      2.48167 |     -0.00142698  |\n",
      "| 2013-05-31 |      2.55521 |      0.00252201  |\n",
      "| 2013-06-30 |      2.40088 |     -0.00869279  |\n",
      "| 2013-07-31 |      2.38067 |      0.000404994 |\n",
      "| 2013-08-31 |      2.40453 |      0.00236244  |\n",
      "| 2013-09-30 |      2.44557 |      0.00195008  |\n",
      "| 2013-10-31 |      2.38996 |     -0.000796258 |\n",
      "| 2013-11-30 |      2.43279 |      0.00176997  |\n",
      "| 2013-12-31 |      2.40286 |     -0.00214924  |\n",
      "| 2014-01-31 |      2.38204 |     -0.00186436  |\n",
      "| 2014-02-28 |      2.392   |      0.000766    |\n",
      "| 2014-03-31 |      2.36454 |     -0.000478774 |\n",
      "| 2014-04-30 |      2.3397  |     -0.000122828 |\n",
      "| 2014-05-31 |      2.35137 |      0.00034055  |\n",
      "| 2014-06-30 |      2.3707  |      0.000244924 |\n",
      "| 2014-07-31 |      2.44469 |      0.00316653  |\n",
      "| 2014-08-31 |      2.41955 |      0.000366214 |\n",
      "| 2014-09-30 |      2.48945 |      0.00308844  |\n",
      "| 2014-10-31 |      2.483   |      0.0013461   |\n",
      "| 2014-11-30 |      2.57935 |      0.00520594  |\n",
      "| 2014-12-31 |      2.74497 |      0.00840219  |\n",
      "| 2015-01-31 |      2.75323 |     -4.80752e-05 |\n",
      "| 2015-02-28 |      2.79563 |      0.00213918  |\n",
      "| 2015-03-31 |      2.96238 |      0.00573313  |\n",
      "| 2015-04-30 |      3.1139  |      0.00822268  |\n",
      "| 2015-05-31 |      3.1761  |      0.00220127  |\n",
      "| 2015-06-30 |      3.09293 |     -0.00303757  |\n",
      "| 2015-07-31 |      2.91955 |     -0.00606776  |\n",
      "| 2015-08-31 |      2.71499 |     -0.00560958  |\n",
      "| 2015-09-30 |      2.66466 |     -0.00222759  |\n",
      "| 2015-10-31 |      2.81504 |      0.00617381  |\n",
      "| 2015-11-30 |      2.86467 |      0.00104039  |\n",
      "| 2015-12-31 |      2.92464 |      0.00124691  |\n",
      "| 2016-01-31 |      2.64938 |     -0.0122203   |\n",
      "| 2016-02-29 |      2.62452 |     -0.00088612  |\n",
      "| 2016-03-31 |      2.78248 |      0.00494664  |\n",
      "| 2016-04-30 |      2.75737 |     -0.00105739  |\n",
      "| 2016-05-31 |      2.75882 |     -0.000271003 |\n",
      "| 2016-06-30 |      2.78558 |      0.000271896 |\n",
      "| 2016-07-31 |      2.79093 |      0.000837039 |\n",
      "| 2016-08-31 |      2.86515 |      0.00154974  |\n",
      "| 2016-09-30 |      2.84171 |     -0.00130178  |\n",
      "| 2016-10-31 |      2.80739 |      0.00198266  |\n",
      "| 2016-11-30 |      2.85236 |      0.00216022  |\n",
      "| 2016-12-31 |      2.79988 |     -0.00206424  |\n",
      "| 2017-01-31 |      2.81009 |      0.000997062 |\n",
      "| 2017-02-28 |      2.8406  |      0.00144759  |\n",
      "| 2017-03-31 |      2.87557 |     -0.000245822 |\n",
      "| 2017-04-30 |      2.80262 |     -0.00116012  |\n",
      "| 2017-05-31 |      2.7889  |     -0.000581863 |\n",
      "| 2017-06-30 |      2.82841 |      0.00109712  |\n",
      "| 2017-07-31 |      2.85943 |      0.00120217  |\n",
      "| 2017-08-31 |      2.86954 |      0.00117263  |\n",
      "| 2017-09-30 |      2.87827 |     -0.000164712 |\n",
      "| 2017-10-31 |      2.85197 |      0.000780677 |\n",
      "| 2017-11-30 |      2.83344 |     -0.00100895  |\n",
      "| 2017-12-31 |      2.83961 |     -0.000125386 |\n",
      "| 2018-01-31 |      2.89129 |      0.00234561  |\n",
      "| 2018-02-28 |      2.83347 |     -0.00422827  |\n",
      "| 2018-03-31 |      2.80412 |     -0.00123207  |\n",
      "| 2018-04-30 |      2.72708 |     -0.00148796  |\n",
      "| 2018-05-31 |      2.73205 |      0.000238135 |\n",
      "| 2018-06-30 |      2.64982 |     -0.00409971  |\n",
      "| 2018-07-31 |      2.65876 |      0.00053389  |\n",
      "| 2018-08-31 |      2.56365 |     -0.00226467  |\n",
      "| 2018-09-30 |      2.59129 |      0.0018818   |\n",
      "| 2018-10-31 |      2.48272 |     -0.00422618  |\n",
      "| 2018-11-30 |      2.49369 |     -0.000186659 |\n",
      "| 2018-12-31 |      2.45582 |     -0.00180808  |\n",
      "| 2019-01-31 |      2.49732 |      0.00165616  |\n",
      "| 2019-02-28 |      2.637   |      0.00878189  |\n",
      "| 2019-03-31 |      2.69829 |      0.00250917  |\n",
      "| 2019-04-30 |      2.6663  |     -0.000121513 |\n",
      "| 2019-05-31 |      2.60595 |     -0.00284623  |\n",
      "| 2019-06-30 |      2.63743 |      0.00148715  |\n",
      "| 2019-07-31 |      2.62776 |     -0.000642096 |\n",
      "| 2019-08-31 |      2.60631 |     -0.000677637 |\n",
      "| 2019-09-30 |      2.61536 |      0.000357868 |\n",
      "| 2019-10-31 |      2.61728 |      0.000475931 |\n",
      "| 2019-11-30 |      2.60221 |     -0.000914965 |\n",
      "| 2019-12-31 |      2.67077 |      0.00276031  |\n",
      "| 2020-01-31 |      2.66171 |     -0.00147776  |\n",
      "| 2020-02-29 |      2.65555 |     -0.00140558  |\n",
      "| 2020-03-31 |      2.59739 |     -0.00193264  |\n",
      "| 2020-04-30 |      2.68606 |      0.0019046   |\n",
      "| 2020-05-31 |      2.68754 |     -0.000127245 |\n",
      "| 2020-06-30 |      2.75831 |      0.00229589  |\n",
      "| 2020-07-31 |      2.87299 |      0.00473351  |\n",
      "| 2020-08-31 |      2.92535 |      0.00126829  |\n",
      "| 2020-09-30 |      2.8718  |     -0.00239627  |\n",
      "| 2020-10-31 |      2.83257 |      0.000172929 |\n",
      "| 2020-11-30 |      2.87481 |      0.00244209  |\n",
      "| 2020-12-31 |      2.9121  |      0.00106864  |\n",
      "| 2021-01-31 |      2.92026 |      0.000189982 |\n",
      "| 2021-02-28 |      2.91911 |      0.000563537 |\n",
      "| 2021-03-31 |      2.87417 |     -0.000759217 |\n",
      "| 2021-04-30 |      2.78795 |      8.91041e-05 |\n",
      "| 2021-05-31 |      2.83946 |      0.00268869  |\n",
      "| 2021-06-30 |      2.84565 |     -0.000303999 |\n",
      "| 2021-07-31 |      2.79237 |     -0.00246709  |\n",
      "| 2021-08-31 |      2.76192 |      0.0019644   |\n",
      "| 2021-09-30 |      2.76762 |      0.000378948 |\n",
      "| 2021-10-31 |      2.75024 |     -0.000344315 |\n",
      "| 2021-11-30 |      2.76328 |      0.000227817 |\n",
      "| 2021-12-31 |      2.78229 |      0.000939132 |\n",
      "| 2022-01-31 |      2.69439 |     -0.00413923  |\n",
      "| 2022-02-28 |      2.7222  |      0.00188776  |\n",
      "| 2022-03-31 |      2.63052 |     -0.00257371  |\n",
      "| 2022-04-30 |      2.5377  |     -0.00326259  |\n",
      "| 2022-05-31 |      2.58438 |      0.00240767  |\n",
      "| 2022-06-30 |      2.66558 |      0.00311249  |\n",
      "| 2022-07-31 |      2.62143 |     -0.00204917  |\n",
      "| 2022-08-31 |      2.5771  |     -0.000650774 |\n",
      "| 2022-09-30 |      2.51298 |     -0.00267807  |\n",
      "| 2022-10-31 |      2.4549  |     -0.00269961  |\n",
      "| 2022-11-30 |      2.54106 |      0.00394148  |\n",
      "| 2022-12-31 |      2.52677 |     -0.000878098 |\n",
      "| 2023-01-31 |      2.59215 |      0.00329586  |\n",
      "| 2023-02-28 |      2.59621 |      0.0003949   |\n",
      "| 2023-03-31 |      2.59367 |     -6.56184e-05 |\n",
      "| 2023-04-30 |      2.60733 |      0.000832279 |\n",
      "| 2023-05-31 |      2.57088 |     -0.00178151  |\n",
      "| 2023-06-30 |      2.57567 |     -1.43208e-05 |\n",
      "| 2023-07-31 |      2.60222 |      0.00134087  |\n",
      "| 2023-08-31 |      2.56306 |     -0.00228841  |\n",
      "| 2023-09-30 |      2.55734 |     -0.000128875 |\n",
      "| 2023-10-31 |      2.54417 |     -0.00172877  |\n",
      "| 2023-11-30 |      2.54486 |      0.000175163 |\n",
      "| 2023-12-31 |      2.52709 |     -0.000844111 |\n",
      "+------------+--------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "\n",
    "\n",
    "def read_and_preprocess_data():\n",
    "    \"\"\"\n",
    "    读取市盈率PE数据和股市股票价格数据，计算月度均值和收益率，选取2017-2023年数据\n",
    "    \"\"\"\n",
    "    # 读取priceratio.csv文件中的市盈率（PE）数据\n",
    "    pe_data = pd.read_csv('priceratio.csv')\n",
    "    # 将日期格式化为每月最后一天，并设置为索引\n",
    "    pe_data['month'] = pd.to_datetime(pe_data['month'], format='%Y-%m-%d') + pd.offsets.MonthEnd(1)\n",
    "    pe_data.set_index('month', inplace=True)\n",
    "\n",
    "    # 读取000001.csv文件中的股市数据\n",
    "    stock_data = pd.read_csv('000001.csv')\n",
    "    # 将日期格式化为日期时间格式，并设置为索引\n",
    "    stock_data['Day'] = pd.to_datetime(stock_data['Day'], format='%Y/%m/%d')\n",
    "    stock_data.set_index('Day', inplace=True)\n",
    "\n",
    "    # 选取2013年到2023年的数据\n",
    "    start_date = '2013-01-01'\n",
    "    end_date = '2023-12-31'\n",
    "    pe_data = pe_data[start_date:end_date]\n",
    "    stock_data = stock_data[start_date:end_date]\n",
    "\n",
    "    # 计算股市月度收益率\n",
    "    stock_data['Return'] = stock_data['Close'].pct_change()\n",
    "    monthly_return = stock_data['Return'].resample('M').mean()\n",
    "\n",
    "    # 计算市盈率pe的月度均值\n",
    "    monthly_pe = pe_data['pe'].resample('M').mean()\n",
    "\n",
    "    # 将monthly_pe和monthly_return转换为DataFrame以便更好展示\n",
    "    monthly_pe_df = pd.DataFrame(monthly_pe)\n",
    "    monthly_pe_df.columns = ['Monthly PE']\n",
    "    monthly_return_df = pd.DataFrame(monthly_return)\n",
    "    monthly_return_df.columns = ['Monthly Return']\n",
    "\n",
    "    # 合并两个DataFrame以便一起展示\n",
    "    result_df = pd.concat([monthly_pe_df, monthly_return_df], axis=1)\n",
    "\n",
    "    # 将索引（日期列）格式化为只显示年月日\n",
    "    result_df.index = result_df.index.strftime('%Y-%m-%d')\n",
    "\n",
    "    # 使用tabulate以表格形式展示结果\n",
    "    print(tabulate(result_df, headers='keys', tablefmt='psql'))\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    read_and_preprocess_data()"
   ]
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    "模型构建"
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   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "\n",
    "# 关闭所有无关警告和输出\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "plt.ioff()  # 关闭交互式绘图的自动输出\n",
    "\n",
    "# 设置中文字体（解决中文显示和Glyph警告问题）\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']\n",
    "mpl.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "\n",
    "price = pd.read_csv('priceratio.csv')\n",
    "data = pd.read_csv('000001.csv')\n",
    "\n",
    "# 数据预处理\n",
    "price['month'] = pd.to_datetime(price['month'], format='%Y-%m-%d') + pd.offsets.MonthEnd(1)\n",
    "price.set_index('month', inplace=True)\n",
    "\n",
    "data['Day'] = pd.to_datetime(data['Day'], format='%Y/%m/%d')\n",
    "data.set_index('Day', inplace=True)\n",
    "data['Close'] = pd.to_numeric(data['Close'])\n",
    "data['Preclose'] = data['Close'].shift(1)\n",
    "data['Return'] = data['Close'] / data['Preclose'] - 1\n",
    "\n",
    "# 选取数据\n",
    "start_date = '2013-01-01'\n",
    "end_date = '2023-12-31'\n",
    "monthly_pe = price['pe'][start_date:end_date]\n",
    "\n",
    "# 计算月度收益率\n",
    "monthly_return = data['Return'].resample('M').mean()[start_date:end_date]\n",
    "\n",
    "# 绘制月度市盈率的二维图像\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.plot(monthly_pe.index, monthly_pe.values, marker='o', label='月度市盈率', color='green')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('月度市盈率')\n",
    "plt.title('月度市盈率变化趋势')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show();  # 加分号隐藏对象输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         Monthly_Return   R-squared:                       0.036\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     4.798\n",
      "Date:                Thu, 20 Nov 2025   Prob (F-statistic):             0.0303\n",
      "Time:                        12:23:41   Log-Likelihood:                 590.67\n",
      "No. Observations:                 132   AIC:                            -1177.\n",
      "Df Residuals:                     130   BIC:                            -1172.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0081      0.004     -2.140      0.034      -0.016      -0.001\n",
      "Monthly_PE     0.0031      0.001      2.190      0.030       0.000       0.006\n",
      "==============================================================================\n",
      "Omnibus:                       20.103   Durbin-Watson:                   1.693\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               70.239\n",
      "Skew:                          -0.397   Prob(JB):                     5.59e-16\n",
      "Kurtosis:                       6.484   Cond. No.                         47.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 读取股票数据\n",
    "stock_data = pd.read_csv('000001.csv')\n",
    "# 将日期格式化为日期时间格式，并设置为索引\n",
    "stock_data['Day'] = pd.to_datetime(stock_data['Day'], format='%Y/%m/%d')\n",
    "stock_data.set_index('Day', inplace=True)\n",
    "\n",
    "# 计算股市月度收益率\n",
    "stock_data['Return'] = stock_data['Close'].pct_change()\n",
    "monthly_return = stock_data['Return'].resample('M').mean()\n",
    "\n",
    "# 读取市盈率数据\n",
    "pe_data = pd.read_csv('priceratio.csv')\n",
    "# 将日期格式化为每月最后一天，并设置为索引\n",
    "pe_data['month'] = pd.to_datetime(pe_data['month'], format='%Y-%m-%d') + pd.offsets.MonthEnd(1)\n",
    "pe_data.set_index('month', inplace=True)\n",
    "\n",
    "# 计算市盈率pe的月度均值\n",
    "monthly_pe = pe_data['pe'].resample('M').mean()\n",
    "\n",
    "# 将monthly_pe和monthly_return转换为DataFrame以便更好展示\n",
    "monthly_pe_df = pd.DataFrame(monthly_pe)\n",
    "monthly_pe_df.columns = ['Monthly_PE']\n",
    "monthly_return_df = pd.DataFrame(monthly_return)\n",
    "monthly_return_df.columns = ['Monthly_Return']\n",
    "\n",
    "# 合并两个DataFrame以便一起展示\n",
    "result_df = pd.concat([monthly_pe_df, monthly_return_df], axis=1)\n",
    "result_df.dropna(inplace=True)\n",
    "\n",
    "# 筛选数据\n",
    "start_date = '2013-01-01'\n",
    "end_date = '2023-12-31'\n",
    "result_df = result_df[(result_df.index >= start_date) & (result_df.index <= end_date)]\n",
    "\n",
    "# 建立最小二乘回归模型\n",
    "model = smf.ols('Monthly_Return ~ Monthly_PE', data=result_df).fit()\n",
    "\n",
    "# 输出回归结果摘要\n",
    "print(model.summary())"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "线性方程为：月度收益率 = -0.00806487724840871 + 0.003074366115954983 * 月度市盈率\n"
     ]
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   "source": [
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "plt.ioff()\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "\n",
    "# 获取回归系数\n",
    "intercept = model.params['Intercept']\n",
    "slope = model.params['Monthly_PE']\n",
    "print(f\"线性方程为：月度收益率 = {intercept} + {slope} * 月度市盈率\")\n",
    "\n",
    "# 绘制散点图和拟合直线\n",
    "plt.scatter(result_df['Monthly_PE'], result_df['Monthly_Return'], color='green')\n",
    "plt.plot(result_df['Monthly_PE'], intercept + slope * result_df['Monthly_PE'], color='green', linewidth=2)\n",
    "plt.xlabel('月度市盈率')\n",
    "plt.ylabel('月度收益率')\n",
    "plt.title('月度市盈率 vs 月度收益率回归拟合图')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      Next_Month_Return   R-squared:                       0.021\n",
      "Model:                            OLS   Adj. R-squared:                  0.013\n",
      "Method:                 Least Squares   F-statistic:                     2.745\n",
      "Date:                Thu, 20 Nov 2025   Prob (F-statistic):              0.100\n",
      "Time:                        12:23:41   Log-Likelihood:                 585.20\n",
      "No. Observations:                 132   AIC:                            -1166.\n",
      "Df Residuals:                     130   BIC:                            -1161.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0063      0.004     -1.610      0.110      -0.014       0.001\n",
      "Monthly_PE     0.0024      0.001      1.657      0.100      -0.000       0.005\n",
      "==============================================================================\n",
      "Omnibus:                       11.369   Durbin-Watson:                   1.829\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               27.773\n",
      "Skew:                          -0.169   Prob(JB):                     9.32e-07\n",
      "Kurtosis:                       5.222   Cond. No.                         47.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "线性方程为：下一期月度收益率 = -0.006321777907147804 + 0.0024238785591463785 * 当期月度市盈率\n"
     ]
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       "<Figure size 640x480 with 1 Axes>"
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   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "# 关闭路径、Glyph等无关警告，避免多余输出\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "plt.ioff()  # 关闭绘图对象自动输出\n",
    "# 配置中文字体，解决中文显示问题\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']\n",
    "mpl.rcParams['axes.unicode_minus'] = False  # 解决负号显示异常\n",
    "plt.rcParams['font.family'] = 'SimHei'\n",
    "\n",
    "# 读取股票数据\n",
    "stock_data = pd.read_csv('000001.csv')\n",
    "# 将日期格式化为日期时间格式\n",
    "stock_data['Day'] = pd.to_datetime(stock_data['Day'], format='%Y/%m/%d')\n",
    "stock_data.set_index('Day', inplace=True)\n",
    "\n",
    "# 计算股市月度收益率\n",
    "stock_data['Next_Month_Return'] = stock_data['Close'].pct_change().shift(-1)\n",
    "monthly_return = stock_data['Next_Month_Return'].resample('M').mean()\n",
    "\n",
    "# 读取市盈率数据\n",
    "pe_data = pd.read_csv('priceratio.csv')\n",
    "# 将日期格式化为每月最后一天\n",
    "pe_data['month'] = pd.to_datetime(pe_data['month'], format='%Y-%m-%d') + pd.offsets.MonthEnd(1)\n",
    "pe_data.set_index('month', inplace=True)\n",
    "\n",
    "monthly_pe = pe_data['pe'].resample('M').mean()\n",
    "\n",
    "# 将monthly_pe和monthly_return转换为DataFrame以便更好展示\n",
    "monthly_pe_df = pd.DataFrame(monthly_pe)\n",
    "monthly_pe_df.columns = ['Monthly_PE']\n",
    "monthly_return_df = pd.DataFrame(monthly_return)\n",
    "monthly_return_df.columns = ['Next_Month_Return']\n",
    "result_df = pd.concat([monthly_pe_df, monthly_return_df], axis=1)\n",
    "\n",
    "result_df.dropna(inplace=True)\n",
    "start_date = '2013-01-01'\n",
    "end_date = '2023-12-31'\n",
    "result_df = result_df[(result_df.index >= start_date) & (result_df.index <= end_date)]\n",
    "\n",
    "# 建立最小二乘回归模型\n",
    "model = smf.ols('Next_Month_Return ~ Monthly_PE', data=result_df).fit()\n",
    "print(model.summary())\n",
    "\n",
    "# 获取回归系数\n",
    "intercept = model.params['Intercept']\n",
    "slope = model.params['Monthly_PE']\n",
    "print(f\"线性方程为：下一期月度收益率 = {intercept} + {slope} * 当期月度市盈率\")\n",
    "\n",
    "# 绘制散点图和拟合直线\n",
    "plt.scatter(result_df['Monthly_PE'], result_df['Next_Month_Return'], color='green', alpha=0.6)  # 散点绿色（加透明度更清晰）\n",
    "plt.plot(result_df['Monthly_PE'], intercept + slope * result_df['Monthly_PE'], color='green', linewidth=2)  # 拟合线绿色\n",
    "plt.xlabel('当期月度市盈率')  \n",
    "plt.ylabel('下一期月度收益率')  \n",
    "plt.title('月度市盈率与下一期月度收益率回归拟合图') \n",
    "plt.show(); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  回归模型核心方程为：下一期月度收益率（Next_Month_Return）= -0.00632 + 0.00242 × 当期月度市盈率（Monthly_PE）。\n",
    "  从统计检验结果来看，模型中当期月度市盈率变量的P值高于0.1，这表明在常用的统计显著性水平下，当期市盈率对下一期股票收益率的影响未能通过显著性检验，二者的关联在统计层面不具备可靠性。\n",
    "  从回归方程的系数符号可明确：当期月度市盈率与下一期月度收益率呈现正相关关系，即当期市盈率每提升1个单位，下一期月度收益率理论上会对应增加0.00242个单位。一方面，行业景气度上升会推动企业盈利增长与市盈率抬升，而高景气行业对大盘的贡献度提升，进而带动下一期大盘收益率上涨；另一方面，企业高市盈率所传递的乐观信号可能扩散至全市场，改善投资者对后续行情的预期，最终助推下一期股市收益率走高，反之则会产生反向影响。 "
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